Algorithms and Heuristics
 | This page is incomplete. |
| Algorithms and Heuristics | |
|---|---|
| Type | Inquiry |
| Category | Study |
| Event Information | |
| Participants | 2 |
| Approx. Time | 50 minutes |
| Allowed Resources |
Up to two computers containing information stored so that it is available offline |
Algorithms and Heuristics is an unofficial trial event which has been run twice as a a part of the Scioly.org Monthly Event Challenges (SMEC). It was run as an open-internet event in order to make it more accessible to competitors of all levels. The event is divided into two parts, with a minimum of five algorithm/graph theory problems and one heuristic challenge. Problems may be solved in C++, Java, or Python.
More information can be found in the event rules.
The Event
Algorithms and Heuristics is divided into two parts—the Algorithmic Tasks and the Heuristic Challenge. In Part I, competitors are given a minimum of five algorithmic tasks on the topics specified in the rules. Participants can make an unlimited number of submissions for each task, with the most recent submission being the one that will be scored for the task. In Part II, a heuristic challenge based on the travelling salesman problem will be given to competitors. This challenge differs from Part I in that only five submissions are allowed, with the final score reflecting the highest scoring submission.
General Algorithms
Graph Theory
Scoring
Highest score wins, with the final score (FS) being a combination of the task score (TS) and challenge score (CS). The maximum specified FS is 100 points. The TS is calculated by dividing the competitor's Part I score by the highest Part I score for all teams, and multiplying that score by 60 points. The CS is calculated by dividing the competitor's Part II score by the highest Part II score for all teams, and multiplying that score by 40 points.
In the event of a tie, the team with the highest TS wins. Further ties will be broken using select tasks from Part I.
Resources
Heuristic Challenge (TSP) Resources
- Good blog for TSP basics.
- Collection of TSP datasets.
- Interesting paper on new heuristic algorithm for TSP.
- Paper on using ant colonies as the heuristic algorithm for TSP.